Accurate and efficient polyp detection in wireless capsule endoscopy images

ABSTRACT

A method for detecting polyps in endoscopy images includes pruning a plurality of two dimensional digitized images received from an endoscopy apparatus to remove images that are unlikely to depict a polyp, where a plurality of candidate images remains that are likely to depict a polyp, pruning non-polyp pixels that are unlikely to be part of a polyp depiction from the candidate images, detecting polyp candidates in the pruned candidate images, extracting features from the polyp candidates, and performing a regression on the extracted features to determine whether the polyp candidate is likely to be an actual polyp.

CROSS REFERENCE TO RELATED UNITED STATES APPLICATIONS

This application claims priority from “Towards Accurate and EfficientPolyp Detection in Wireless Capsule Endoscopy Images”, U.S. ProvisionalApplication No. 61/873,412 of Jia, et al., filed Sep. 4, 2013, thecontents of which are herein incorporated by reference in theirentirety.

TECHNICAL FIELD

This disclosure is directed to methods and systems for detecting polypsin images acquired through Wireless Capsule Endoscopy.

DISCUSSION OF THE RELATED ART

A polyp is an abnormal growth of tissue protruding from a mucousmembrane. Bowel cancer may develop from bowel polyps. Detecting cancersat their early stages by means of polyp diagnosis is important in curingcancers. The Wireless Capsule Endoscopy (WCE) imaging technique canprovide a painless and non-invasive way to examine the gastrointestinaltract and can be utilized to detect polyps, and has become well acceptedby clinicians and patients. WCE uses a capsule the size and shape of apill that contains a tiny camera. After a patient swallows the capsule,it takes pictures of the inside of the gastrointestinal tract. Thecaptured images are wirelessly transmitted to an external receiver wornby or near the patient using an appropriate frequency band. Thecollected images may then be transferred to a computer for diagnosis,review and display. A WCE video of a single gastrointestinal tract examcan contain up to 50,000-60,000 2-dimensional images. Manuallyinspecting such a significant number of WCE images is tedious, errorprone, and represents a burden for clinicians. Accurate and fastcomputer-aided polyp detection algorithms is useful. However, due tonon-uniform illumination from the light emitting diodes in the capsule,disturbances such as bubbles and trash liquids, the complexity of theanatomy inside the bowel, and large variations in the size and shape andsize of the polyps, accurate polyp detection from WCE images is achallenging task. WCE is a new field of research, and thus few papershave been published that deal with polyp detection in WCE images.

SUMMARY

Exemplary embodiments of the disclosure as described herein are directedto new polyp detection methods for Wireless Capsule Endoscopy (WCE)images. A method according to an embodiment of the disclosure includesthe steps of greenish image pruning, pixel-wise pruning, initial polypcandidate localization and a regression based polyp detection. Methodsaccording to embodiments of the disclosure were validated using threegroups of 2-fold cross validation on 27984 images. On average, a 0.648true positive rate was achieved with a 0.1 false positive rate. Adetection process according to an embodiment of the disclosure executedin 0.83 seconds per image on average.

According to an embodiment of the disclosure, there is provided a methodfor detecting polyps in endoscopy images, including pruning a pluralityof two dimensional digitized images received from an endoscopy apparatusto remove images that are unlikely to depict a polyp, where a pluralityof candidate images remains that are likely to depict a polyp, pruningnon-polyp pixels that are unlikely to be part of a polyp depiction fromthe candidate images, detecting polyp candidates in the pruned candidateimages, extracting features from the polyp candidates, and performing aregression on the extracted features to determine whether the polypcandidate is likely to be an actual polyp.

According to a further aspect of the disclosure, pruning non-polyppixels from the candidate images comprises calculating a posteriorBayesian probability of a pixel i being a polyp pixel

${{{g\left( x_{i} \right)} \equiv {P\left( {x_{i}\mspace{14mu}{is}\mspace{14mu}{polyp}} \right)}} = \frac{{P({polyp})}{f_{P}\left( x_{i} \right)}}{{{P({polyp})}{f_{P}\left( x_{i} \right)}} + {{P({normal})}{f_{N}\left( x_{i} \right)}}}},$where x_(i) is an N_(c) dimensional vector associated with pixel i thatincludes intensity values from different color spaces, P (polyp) andP(normal) denote the prior probability that pixel i is a polyp pixel ora non-polyp pixel respectively, and f_(P) and f_(N) are distributionfunctions with respect to x_(i), given that pixel i is either polyp ornon-polyp, respectively, where if g(x_(i)) is less than a predeterminedthreshold, pixel i is determined to be a non-polyp pixel.

According to a further aspect of the disclosure, N_(c)=3 is a number ofcolor channels, the color channels are (BUH), where B is selected froman RGB color space, U is selected from a LUV color space, and H isselected from a HSV color space.

According to a further aspect of the disclosure, g(x_(i)) isapproximated by

$\frac{f_{P}\left( x_{i} \right)}{f_{N}\left( x_{i} \right)}.$

According to a further aspect of the disclosure, polyp candidates for animage I_(k) comprise a plurality of ellipses {(E_(k) ^(l), p_(k)^(l))}_(l=1) ^(N) ^(k) for each image I_(k), where ellipse E_(k) ^(l) isa polyp region-of-interest with a polyp probability p_(k) ^(l) and N_(k)is the number of ellipses for image I_(k).

According to a further aspect of the disclosure, features extracted fromthe polyp candidates include geometric features and appearance basedfeatures, where the geometric features include an area of each ellipse,and a ratio of the major axis length and the minor axis length of eachellipse, and where the appearance based features are extracted from 3concentric regions-of-interest (ROIs) about the detected ellipse.

According to a further aspect of the disclosure, appearance basedfeatures include a multi-scaled rotational invariant local binarypattern where each of a plurality of circular neighbors of a pixel i arelabeled as 1 if the intensity of the neighbor is greater than that ofpixel i, and 0 otherwise, and a binary number formed by the values ofthe plurality of neighbors is to classified into a bin based on a numberof consecutive 1's and consecutive 0's in each binary number, where thebinary number is classified into a separate bin if there are noconsecutive 1's and consecutive 0's in the binary number, where each binis a feature.

According to a further aspect of the disclosure, appearance basedfeatures include a histogram of oriented gradients (HOG) calculated bycomputing image gradients inside each ROI, and assigning a magnitude ofeach gradient to a direction bin based on an orientation of each polypcandidate ellipse.

According to a further aspect of the disclosure, appearance basedfeatures further include calculating a dissimilarity of HOG featuresbetween two ROIs from d(f,g)=Σ_(i=1) ^(N)|f_(i)−g_(i)|, where f and gare HOG histograms for the two regions, respectively, ∥ is an L_(I)norm, and N is a number of direction bins, and calculating a colordistribution dissimilarity between two regions from d(f,g)=Σ_(i=1)^(N)|f_(i)−g_(i)|, where f and g are intensity distribution histogramsfor the two regions, respectively, for each color.

According to a further aspect of the disclosure, performing a regressionon the extracted features comprises solving Y≈f(X, β), where Y is atarget variable, X is a vector of the extracted features, and β arepredetermined parameters calculated during a training stage, wheretarget value y_(k) ^(l) for the l-th polyp candidate of image I_(k) isdefined as an overlap ratio between the ellipse E_(k) ^(l) of the polypcandidate and a ground truth ellipse E_(k) ^(g)

$y_{k}^{l} = \left\{ {\begin{matrix}{\min\left( {\frac{{E_{k}^{g}\bigcap E_{k}^{l}}}{E_{k}^{g}},\frac{{E_{k}^{g}\bigcap E_{k}^{l}}}{E_{k}^{l}}} \right)} & {I_{k}\mspace{14mu}{is}\mspace{14mu}{positive}\mspace{14mu}{image}} \\0 & {I_{k}\mspace{14mu}{is}\mspace{14mu}{negative}\mspace{14mu}{image}}\end{matrix},} \right.$where ∥ represents an area of the argument ellipse.

According to a further aspect of the disclosure, f(X, β) is a supportvector regressor.

According to a further aspect of the disclosure, the method includescalculating an image-wise polyp score S_(k) that represents a likelihoodthat an image I_(k) contains one or more polyps from S_(k)=Σ_(l=1) ^(Ñ)^(k) (w·p_(k) ^(l)+(1−w)·y_(k) ^(l)), where p_(k) ^(l) is theprobability of candidate ellipse E_(k) ^(l) being a polyp where {p_(k)^(l)}_(l=1) ^(N) ^(k) is sorted in descending order and a top Ñ_(k)detected polyp candidates are selected, y_(k) ^(l) is the correspondingregression target value, and w is a combining weight determined in atraining stage to achieve a largest true positive rate at apredetermined false positive rate.

According to another embodiment of the disclosure, there is provided amethod for training a detector that detects polyps in endoscopy imagesthat includes detecting polyp candidates in a plurality of twodimensional digitized images received from an endoscopy apparatus, wherepolyp candidates for an image I_(k) comprise a plurality of ellipses{(E_(k) ^(l), p_(k) ^(l))}_(l=1) ^(n) ^(k) for each image I_(k), whereellipse E_(k) ^(l) is a polyp region-of-interest with a polypprobability p_(k) ^(l) and N_(k) is the number of ellipses for imageI_(k), extracting features from the polyp candidates, calculating targetvalues Y for a regression model represented by Y≈f(X, β), where X is avector of features, and β are unknown parameters to be determined, anddetermining the parameters β for the regression model from the targetvalues Y and feature vector X.

According to a further aspect of the disclosure, target value y_(k) ^(l)for the l-th polyp candidate of image I_(k) is defined as an overlapratio between the ellipse E_(k) ^(l) of the polyp candidate and a groundtruth ellipse E_(k) ^(g)

$y_{k}^{l} = \left\{ {\begin{matrix}{\min\left( {\frac{{E_{k}^{g}\bigcap E_{k}^{l}}}{E_{k}^{g}},\frac{{E_{k}^{g}\bigcap E_{k}^{l}}}{E_{k}^{l}}} \right)} & {I_{k}\mspace{14mu}{is}\mspace{14mu}{positive}\mspace{14mu}{image}} \\0 & {I_{k}\mspace{14mu}{is}\mspace{14mu}{negative}\mspace{14mu}{image}}\end{matrix},} \right.$where ∥ represents an area of the argument ellipse.

According to a further aspect of the disclosure, features extracted fromthe polyp candidates include geometric features and appearance basedfeatures, where the geometric features include an area of each ellipse,and a ratio of the major axis length and the minor axis length of eachellipse, and where the appearance based features are extracted from 3concentric regions-of-interest (ROIs) about the detected ellipse.

According to a further aspect of the disclosure, appearance basedfeatures include a multi-scaled rotational invariant local binarypattern where each of a plurality of circular neighbors of a pixel i arelabeled as 1 if the intensity if the neighbor is greater than that ofpixel i, and 0 otherwise, and a binary number formed by the values ofthe plurality of neighbors is classified into a bin based on a number ofconsecutive 1's and consecutive 0's in each binary number, where thebinary number is classified into a separate bin if there are noconsecutive 1's and consecutive 0's in the binary number, where each binis a feature.

According to a further aspect of the disclosure, appearance basedfeatures include a histogram of oriented gradients (HOG) calculated bycomputing image gradients inside each ROI, and assigning a magnitude ofeach gradient to a direction bin based on an orientation of each polypcandidate ellipse.

According to a further aspect of the disclosure, appearance basedfeatures further include calculating a dissimilarity of HOG featuresbetween two ROIs from d(f,g)=Σ_(i=1) ^(N)|f_(i)−g_(i)|, where f and gare HOG histograms for the two regions, respectively, ∥ is an L₁ norm,and N is a number of direction bins, and calculating a colordistribution dissimilarity between two regions from d(f,g)=Σ_(i=1)^(N)|f_(i)−g_(i)|, where f and g are intensity distribution histogramsfor the two regions, respectively, for each color.

According to another embodiment of the disclosure, there is provided anon-transitory program storage device readable by a computer, tangiblyembodying a program of instructions executed by the computer to performthe method steps for detecting polyps in endoscopy images.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1(a)-(b) are flowcharts of a training procedure and a testingprocedure, respectively, according to embodiments of the disclosure.

FIG. 2 illustrates the polyp ROIs I, II and III, according to anembodiment of the disclosure.

FIGS. 3(a)-(b) illustrate a standard LBP, and an RI-LBP, according to anembodiment of the disclosure.

FIG. 4 is a flowchart of a method of calculating HOG features for apolyp candidate, according to an embodiment of the disclosure.

FIG. 5 is a table of summary results of the cross validation performancetests on the original positive images, perturbed images, and combinedimages, according to an embodiment of the disclosure.

FIG. 6 is a block diagram of an exemplary computer system forimplementing a method for detecting polyps in Wireless Capsule Endoscopy(WCE) images, according to an embodiment of the disclosure.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Exemplary embodiments of the disclosure as described herein generallyinclude systems and methods for detecting polyps in Wireless CapsuleEndoscopy (WCE) images. Accordingly, while the disclosure is susceptibleto various modifications and alternative forms, specific embodimentsthereof are shown by way of example in the drawings and will herein bedescribed in detail. It should be understood, however, that there is nointent to limit the disclosure to the particular forms disclosed, but onthe contrary, the disclosure is to cover all modifications, equivalents,and alternatives falling within the spirit and scope of the disclosure.

As used herein, the term “image” refers to multi-dimensional datacomposed of discrete image elements (e.g., pixels for 2-D images andvoxels for 3-D images). The image may be, for example, a medical imageof a subject collected by computer tomography, magnetic resonanceimaging, ultrasound, or any other medical imaging system known to one ofskill in the art. The image may also be provided from non-medicalcontexts, such as, for example, remote sensing systems, electronmicroscopy, etc. Although an image can be thought of as a function fromR³ to R or R⁷, methods of embodiments of the disclosure are not limitedto such images, and can be applied to images of any dimension, e.g., a2-D picture or a 3-D volume. For a 2- or 3-dimensional image, the domainof the image is typically a 2- or 3-dimensional rectangular array,wherein each pixel or voxel can be addressed with reference to a set of2 or 3 mutually orthogonal axes. The terms “digital” and “digitized” asused herein will refer to images or volumes, as appropriate, in adigital or digitized format acquired via a digital acquisition system orvia conversion from an analog image.

Exemplary embodiments of the present disclosure are directed to a novelmachine learning based approach to quickly and accurately discriminate aWCE image into a positive, polyp image or a negative, normal image. Analgorithm according to an embodiment of the disclosure first removesimages that unlikely to contain polyps. Then, hypotheses regarding theposition, orientation and scale of the polyps are defined for apolyp-like region-of-interest (ROI) detection, and then a regressionbased approach is used to validate these hypotheses and determinewhether the ROI is a polyp or normal tissue.

A method according to an embodiment of the disclosure can discriminate apolyp image and a normal image. A detection pipeline according to anembodiment of the disclosure may include four components: image-wisepruning, pixel-wise pruning, polyp candidate detection, andregression-based polyp candidate refinement. Image-wise pruning is usedto find greenish images, which are unlikely to be polyp images.Pixel-wise pruning is to remove negative pixels and preserve asufficient number of positive pixels. A result mask corresponding to thepositive pixels is provided to a candidate detection stage. According toan embodiment of the disclosure, candidate detection is performed tolocate polyp candidate positions, orientations and scale. An exemplary,non-limiting method for detecting polyp candidates comprises MarginalSpace Learning. As a result, a set of ellipses {(E_(k) ^(l), p_(k)^(l))}_(l=1) ^(N) ^(k) is generated for each image I_(k), where E_(k)^(l) is a polyp region-of-interest (ROI) with a polyp probability p_(k)^(l) and N_(k) is the number of ellipses generated for image I_(k).Based on the polyp candidates, a regressor according to an embodiment ofthe disclosure, such as Support Vector Regression (SVR), can be utilizedto differentiate polyp and non-polyp images.

Methods according to embodiments of the disclosure include methods forpixel-wise pruning and methods for regression-based polyp detection,although experiments validated four components in one pipeline. Asmachine learning based approaches, methods according to embodiments ofthe disclosure include training and testing stages.

FIGS. 1(a)-(b) are flowcharts of an overview of a training procedure anda testing procedure, respectively, according to embodiments of thedisclosure. Referring now to FIG. 1A, given a set of 2-dimensionaltraining images that are known to depict polyps, a training processaccording to an embodiment of the disclosure begins at step 10 bydetecting polyp candidates in each image. Then, for each image, featuresare extracted from the polyp candidates and ground truth polyps at step11, and a target value for a regression model is calculated from thepolyp candidates and ground truth polyps at step 12. The extractedfeatures and the target values are used to train a regression model atstep 13. An exemplary, non-limiting regression model is support vectorregression.

Referring now to FIG. 1B, given a set of 2-dimensional testing imagesreceived from an endoscopy apparatus, a testing process according to anembodiment of the disclosure begins at step 14 by performing image-wisepruning on the set of images. Image-wise pruning involves pruning orremoving those images from the image set that are unlikely to containpolyps, such as images that are primarily green in color. The SVMclassifier trained in the training phase is used to predict greenishimages. Histograms for the three R, G and B color channels over thewhole image are calculated and are utilized as features by the SVMclassifier. According to an embodiment of the disclosure, 16 bins areused for each color, for a total of 48 features. The SVM classifierreturns a score based on the features, on which the pruning decision isbased. Pixel-wise pruning is performed for each image at step 15, whichinvolves pruning or removing non-polyp pixels from the image. A resultmask corresponding to the positive pixels is provided to a candidatedetection stage. Then, at step 16, polyp candidates are detected in thepruned image to locate polyp candidate positions, orientations andscale, and features are extracted from the polyp candidates at step 17.An exemplary, non-limiting method for detecting polyp candidatescomprises Marginal Space Learning. Marginal Space Learning is disclosedin Zheng, et al., “Four-Chamber Heart Modeling And AutomaticSegmentation For 3-D Cardiac CT Volumes Using Marginal Space LearningAnd Steerable Features”, Medical Imaging, IEEE Transactions on, 27(11)(2008), 1668-1681, the contents of which are herein incorporated byreference in their entirety. As a result, a set of ellipses {(E_(k)^(l), p_(k) ^(l))}_(i=1) ^(N) ^(k) is generated for each image I_(k),where E_(k) ^(l) is a polyp region-of-interest (ROI) with a polypprobability p_(k) ^(l) and N_(k) is the number of ellipses generated forimage I_(k). The extracted features are used with the regression modeltrained by the method of FIG. 1A to determine whether a polyp candidateis an actual polyp, at step 18, and, at step 19, an image-wise score iscalculated for each image from the top polyp candidates in that image.Details of the above steps are provided hereinbelow.

In practice, the normal images outnumber the positive images. Asignificantly unbalanced number of positive and negative images can makethe training and testing challenging. According to an embodiment of thedisclosure, to deal with this situation, positive images are perturbedby rotation and reflection with respect to the image center. Forexample, in one experiment of an embodiment of the disclosure, therewere 541 positive images and 15000 negative images. According to anembodiment of the disclosure, 12 rotation angles, {−10°, −5°, 0°, 5°,10°, 45°, 90°, 135°, 180°, 225°, 270°, 315°} are used. Note that thisnumber is exemplary and non-limiting, and any number of rotation anglescan be used. With reflection, 24 positive samples, including theoriginal image, may be created. According to an embodiment of thedisclosure, interpolation may be used to perturb the mages. However,interpolation blurs images. It is also known that some imagedescriptors, such as a Histogram of Oriented Gradients (HOG), aresensitive to smoothed images. Thus, according to an embodiment of thedisclosure, to reduce the bias caused by smoothed perturbed positiveimages in the training process, every trained normal image is alsorandomly perturbed to one of those 24 positions. According to anembodiment of the disclosure, bilinear interpolation may provide abalanced solution for image perturbation.

Pixel-Wise Pruning

According to an embodiment of the disclosure, pixel-wise pruning may beperformed for the following reasons. (1) The fewer number of processedpixels, the more computationally efficient is the polyp candidatedetection. (2) The likelihood of polyp ROIs being detected can beimproved if a large number of normal pixels are removed and sufficientnumber of polyp pixels are preserved.

According to an embodiment of the disclosure, to conduct pixel-wisepruning, a Bayesian decision based method may be used. The posteriorprobability of a pixel being a polyp pixel i is:

$\begin{matrix}{{{{g\left( x_{i} \right)} \equiv {P\left( {x_{i}\mspace{14mu}{is}\mspace{14mu}{polyp}} \right)}} = \frac{{P({polyp})}{f_{P}\left( x_{i} \right)}}{{{P({polyp})}{f_{P}\left( x_{i} \right)}} + {{P({normal})}{f_{N}\left( x_{i} \right)}}}},} & (1)\end{matrix}$where x_(i) is a vector with N_(c) dimensions at pixel i that includesintensity values from different to color spaces. According to anembodiment of the disclosure, N_(c) is 3. P(polyp) and P(normal) denotethe prior probability and f_(P) and f_(N) are the distribution functionswith respect to x_(i), given that pixel is either polyp or normalrespectively. In a testing stage according to an embodiment of thedisclosure, if g(x_(i))<t_(p), the pixel may be labeled as normal, wheret_(p) is a heuristic threshold determined from the training data.

f_(P) and f_(N) may be described using a 32×32×32 histogram, which maybe learned from the training set. According to an embodiment of thedisclosure, an approximation version

${{\hat{g}(x)} \equiv \frac{f_{P}(x)}{f_{N}(x)}},$is used instead of g(x) to simplify the calculations. According to anembodiment of the disclosure, t_(p) may be determined based on acriterion that the ratio of removed/preserved polyp pixels on thetraining set is between 0.01 and 0.05.

To determine the N_(c)=3 color channels, experiments were conducted onthree color spaces (RGB, LUV, HSV) as well as a mixture from these threecolor spaces. For mixed channels, one channel was selected from eachcolor space which discriminates the polyp pixels and non-polyp pixelsmost effectively. According to an embodiment of the disclosure, R or Bwere selected from RGB (Red, Green, Blue), as it was found that R and Bhave similar performance, U was selected from LUV (Luminance, where Uand V are chromaticites), and H was selected from HSV (Hue, Saturation,Value). Finally, 5 combinations, RGB, LUV, HSV, BUH and RUH, werecompared. From these comparisons, three channels, namely BUH, wereselected as providing a best performance for pixel based pruning.

Regression-Based Polyp Detection

According to an embodiment of the disclosure, the following regressionanalysis may be used to discriminate polyp and normal tissue:Y≈f(X,β)  (2)where Y is the target variable, X is the feature vector, and β areunknown parameters need be determined in the training stage. Consider aset of sample points, {(x₁, y₁), . . . , (x_(l), y_(l))}, wherex_(l)εR^(n) is a feature vector and y_(l)εR¹ is a target value.According to an embodiment of the disclosure, support vector regression(SVR) may be used as a regressor. An exemplary, non-limiting softwarepackage for performing SVR is LIBSVM.

Target Variable: Assume there is a polyp candidate ROI E_(k) ^(l) andalso a ground truth E_(k) ^(g) for image I_(k). The target value y_(k)^(l) for the l-th polyp candidate of image I_(k) is defined as anoverlap ratio between the candidate ROI and ground truth:

$\begin{matrix}{y_{k}^{l} = \left\{ {\begin{matrix}{\min\left( {\frac{{E_{k}^{g}\bigcap E_{k}^{l}}}{E_{k}^{g}},\frac{{E_{k}^{g}\bigcap E_{k}^{l}}}{E_{k}^{l}}} \right)} & {I_{k}\mspace{14mu}{is}\mspace{14mu}{positive}\mspace{14mu}{image}} \\0 & {I_{k}\mspace{14mu}{is}\mspace{14mu}{negative}\mspace{14mu}{image}}\end{matrix},} \right.} & (3)\end{matrix}$where ∥ refers to the area of a ROI. Note that a subset of candidatesare used for training samples. An exemplary, non-limiting number oftraining samples is 100,000. According to an embodiment of thedisclosure, a sampling criterion is to balance the number of negativeand positive candidates. In addition, all positive ground truths areused as training samples, whose target values are set to 1.0.

Feature Extraction: According to an embodiment of the disclosure, bothgeometric and appearance based discriminative features were used foreach polyp candidate. The geometric-based features are defined asfollows: the area and the ratio of the major and minor axis lengths of adetected ROI. To extract appearance-based features, three concentricROIs, denoted as I, II and III are created from a detected ROI.Exemplary, non-limiting scales of these three regions are {0.8; 1.2;1.4} compared to the size of a detected polyp candidate. FIG. 2illustrates the polyp ROIs I, II and III. The dashed curve 20 indicatesthe candidate detection result, and a represents the scale of thecandidate detection result. The idea behind these divisions is that Iindicates that polyp tissue is more likely in this region, III is morelikely to be normal tissue and II indicates an ambiguous boundaryregion. According to an embodiment of the disclosure, three types ofappearance-based features will be described in detail: a multi-scaledRotation Invariant Local Binary Pattern (RI-LBP), a Histogram ofOriented Gradients (HOG), and Color Distribution Dissimilarity.

(1) Multi-Scaled RI-LBP Based Features: FIGS. 3(a)-(b) illustrateexamples of two LBP definitions. FIG. 3(a) illustrates a standard LBP,and FIG. 3(b) illustrates an RI-LBP. In FIGS. 3(a)-(b), gray dotsrepresent 1, and black dots represent 0. As depicted in FIG. 3(a), N₁circular neighbors of each pixel i in a ROI with a radius r areconsidered. Each neighbor has a label. A neighbor is labeled as 1 if itsvalue v is greater than i's, otherwise it is labeled as 0. The value vcan be a gray value or an intensity value from a different colorspace.An N_(i)-bit binary number, counting from 0° in a clockwise direction,is converted to a decimal number. In the example of FIG. 3(a), thebinary number 10011000 is converted to its decimal equivalent, 152. Ahistogram of all decimal numbers in the ROI is calculated. Thenormalized histogram bin represents a specific local pattern, which canbe utilized as a feature.

FIG. 3(b) illustrates a rotational invariant LBP (RI-LBP) algorithm,which can also reduce the dimensionality of the features. Instead ofbeing converted to a decimal number, the binary number is classifiedinto N_(l)+2 categories ({0, . . . , N_(l)+1}). If the binary number hasnonconsecutive 1s and non-consecutive 0s, then it is classified into thecategory N_(l)+1, otherwise it is classified into one of the other kcategories, (0≦k≦N_(l)) in terms of either of the following criteria:

(1) The binary number has k consecutive 1s and N_(l)−k 0s;

(2) The binary number has N_(l)−k consecutive 0s and k 1s.

Then a histogram with N_(l)+2 bins is computed by counting theoccurrence frequency of each category. In FIG. 3(b), the upper rowdepicts, from left to right, the representations of numbers 0, 1, 2, . .. , n, and the lower row representations of binary numbers classifiedinto category N_(l)+2.

According to an embodiment of the disclosure, an RI-LBP method isapplied in the regions I, II and III individually. Moreover, amulti-scaled method is used with pixel radii of r={1, 3, 5} pixels. Anexemplary, non-limiting value of N_(l) is 8. Thus, an RI-LBP-basedhistogram of 90 bins is used, in which each bin is a feature.

(2) HOG Based Features: HOG features represent a weighted distributionof the gradient orientations in a local region. A flowchart of a methodof calculating HOG features for to a polyp candidate is presented inFIG. 4. Referring now to the figure, for a polyp candidate E_(k) ^(l),image gradients are computed inside the ROIs I, II and III at step 41,using kernels (−1, 0, 1) in the x-direction and (−1, 0, 1)^(T) in they-direction. Let g_(i) and m_(i) denote gradient direction and magnitudeof a pixel i respectively. Then at step 42, g_(i) is aligned to theorientation of E_(k) ^(i). Since the polyp has a direction, the gradientdirection should be calculated based on polyp direction, which willchange the gradient direction to be aligned with the polyp direction.Let {tilde over (g)}_(i) represent the aligned gradient direction.Inside each ROI, m_(i) is cast to the N_(h) direction bins at step 43,which are uniformly divided between 0° and 180° based on {tilde over(g)}_(i), to obtain a magnitude histogram with N_(h) bins for each ROI.The histograms are normalized over these ROIs. The normalized histogramsrepresent HOG features according to an embodiment of the disclosure.

In addition, the dissimilarity of HOG features of region I (polyptissue) and III (normal tissue) is computed at step 44. Thedissimilarity is defined as follows:d(f,g)=Σ_(i=1) ^(N) |f _(i) −g _(i)|  (4)where f and g are two histograms for regions I and III, respectively, ∥is an L₁ norm, and {f_(i)}_(i=1) ^(N) and {g_(i)}_(i=1) ^(N) are thehistogram representations. For a HOG based dissimilarity, N=N_(h). Anexemplary, non-limiting value of N_(h) is 6. According to an embodimentof the disclosure, three RGB channels were used to compute the HOG basedfeatures. Thus, 54 HOG features and 3 dissimilarity features are used.

(3) Color Distribution Dissimilarity: Polyp and normal tissues havedifferent intensity distribution in different RGB channels. For example,greener pixels are more likely to be residues while redder pixels aremore likely to belong to polyp tissue. According to an embodiment of thedisclosure, the color distribution dissimilarity between two ROI regionsis given by EQ. (4), where {f_(i)}_(i=1) ^(N) and {g_(i)}_(i=1) ^(N) arethe intensity distribution histograms for between the two regions,respectively. Exemplary, non-limiting choice for the regions are I andIII. An exemplary, non-limiting number of bins N is 16, and three RGBchannels were used to achieve 3 color distribution dissimilarityfeatures.

Image-Wise Polyp Score

According to an embodiment of the disclosure, an image-wise polyp scoreS_(k) represents the likelihood that an image I_(k) contains one or morepolyps. According to an embodiment of the disclosure, S_(k) may bedefined as follows:S _(k)=Σ_(l=1) ^(Ñ) ^(k) (w·p _(k) ^(l)+(1−w)·y _(k) ^(l)),  (5)where p_(k) ^(l) is the probability of a candidate E_(k) ^(l) beingpolyp, {p_(k) ^(l)}_(l=1) ^(N) ^(k) is sorted in descending order, wherethe top Ñ_(k) detected polyp candidates are utilized, y_(k) ^(l) is thecorresponding regression target value, and w is a combining weight. Anexemplary, non-limiting value of Ñ_(k) is 30. According to an embodimentof the disclosure, w is optimized in the training stage to achieve thelargest true positive rate (TPR or sensitivity) at a predetermined falsepositive rate (FPR, or 1-specificity). An exemplary, non-limiting valuefor the predetermined false positive rate is 0.1. TPR and FPR aredefined as follows:

$\begin{matrix}{{{TPR} = \frac{{number}\mspace{14mu}{of}\mspace{14mu}{true}\mspace{14mu}{positives}}{{{number}\mspace{14mu}{of}\mspace{14mu}{true}\mspace{14mu}{positives}} + {{number}\mspace{14mu}{of}\mspace{14mu}{false}\mspace{14mu}{positives}}}},} & (6) \\{{FPR} = {\frac{{number}\mspace{14mu}{of}\mspace{14mu}{false}\mspace{14mu}{positives}}{{{number}\mspace{14mu}{of}\mspace{14mu}{true}\mspace{14mu}{positives}} + {{number}\mspace{14mu}{of}\mspace{14mu}{false}\mspace{14mu}{positives}}}.}} & (7)\end{matrix}$Experiments

According to an embodiment of the disclosure, a 2-fold cross validationwas conducted on 12984 positive images (541 original images+12443perturbed images) and 15000 negative images. Note that tests were onlyperformed on non-perturbed negative images. The image size was 256×256.For each fold, images were randomly assigned to two sets Set1 and Set2,so that both sets are equal size. To guarantee the independence oftraining and testing data, the perturbed polyp images were assigned withtheir original image into the same set. The cross validation is repeatedthree times, denoted as Test1, Test2 and Test3. The tests were performedon a standard desktop machine with an Intel Xeon 2.8 GHz CPU and 4 GBRAM. To measure the detection performance, TPR was used at 0.1 FPR.Table 1, shown in FIG. 5, displays a summary of the cross validationperformance tests on the original positive images (Original), perturbedimages (Perturbed) and combined images (Original+Perturbed), where theoverall TPR of Set1 and Set2 for each test is presented at 0.1 FPR. Onaverage, a TPR of 0.637, 0.648 and 0.648 was obtained for the original,perturbed and combined images, respectively. The computation time perimage was 0.83±0.26 (mean±standard deviation) second with a median of0.91 and a maximum of 1.23 second. Note that the training and testingwere performed on 27984 images in the computation efficiency test.

System Implementations

It is to be understood that the present disclosure can be implemented invarious forms of hardware, software, firmware, special purposeprocesses, or a combination thereof. In one embodiment, the presentdisclosure can be implemented in software as an application programtangible embodied on a computer readable program storage device. Theapplication program can be uploaded to, and executed by, a machinecomprising any suitable architecture.

FIG. 6 is a block diagram of an exemplary computer system forimplementing a method for detecting polyps in Wireless Capsule Endoscopy(WCE) images according to an embodiment of the disclosure. Referring nowto FIG. 6, a computer system 61 for implementing an embodiment of thepresent disclosure can comprise, inter alia, a central processing unit(CPU) 62, a memory 63 and an input/output (I/O) interface 64. Thecomputer system 61 is generally coupled through the I/O interface 64 toa display 65 and various input devices 66 such as a mouse and akeyboard. The support circuits can include circuits such as cache, powersupplies, clock circuits, and a communication bus. The memory 63 caninclude random access memory (RAM), read only memory (ROM), disk drive,tape drive, etc., or a combinations thereof. Embodiments of the presentdisclosure can be implemented as a routine 67 that is stored in memory63 and executed by the CPU 62 to process the signal from the signalsource 68. As such, the computer system 61 is a general purpose computersystem that becomes a specific purpose computer system when executingthe routine 67 of the present disclosure.

The computer system 61 also includes an operating system and microinstruction code. The various processes and functions described hereincan either be part of the micro instruction code or part of theapplication program (or combination thereof) which is executed via theoperating system. In addition, various other peripheral devices can beconnected to the computer platform such as an additional data storagedevice and a printing device.

It is to be further understood that, because some of the constituentsystem components and method steps depicted in the accompanying figurescan be implemented in software, the actual connections between thesystems components (or the process steps) may differ depending upon themanner in which an embodiment of the present disclosure is programmed.Given the teachings of embodiments of the present disclosure providedherein, one of ordinary skill in the related art will be able tocontemplate these and similar implementations or configurations of thepresent disclosure.

While embodiments of the present disclosure has been described in detailwith reference to exemplary embodiments, those skilled in the art willappreciate that various modifications and substitutions can be madethereto without departing from the spirit and scope of the disclosure asset forth in the appended claims.

What is claimed is:
 1. An image processing method for detecting theexistence of polyps in endoscopy images obtained from a patient,comprising the steps of, in at least one processor: receiving aplurality of two dimensional digitized images generated by an endoscopyapparatus; removing from said plurality of two dimensional digitizedimages images that are unlikely to depict a polyp, based on results ofpredefined image-wise processing performed on each of said plurality oftwo dimensional digitized images, wherein a plurality of candidateimages remains that are likely to depict a polyp; from each of saidplurality of candidate images, pruning non-polyp pixels that areunlikely to be part of a polyp depiction, based on results of predefinedpixel-wise processing performed on each of said plurality of candidateimages; detecting polyp candidate pixels in the pruned candidate images,based on results of predefined processing performed on pixels of saidpruned candidate images; extracting features from said polyp candidatepixels based on results of predefined processing performed on said polypcandidate pixel; performing a regression on said extracted features todetermine whether said polyp candidate pixels are likely to depict anactual polyp; and identifying candidate images as depicting polyps basedon results of said regression, wherein said identified candidate imagedare used by a clinician to diagnosis a condition of said patient.
 2. Themethod of claim 1, wherein pruning non-polyp pixels from the candidateimages comprises calculating a posterior Bayesian probability of a pixeli being a polyp pixel${{{g\left( x_{i} \right)} \equiv {P\left( {x_{i}\mspace{14mu}{is}\mspace{14mu}{polyp}} \right)}} = \frac{{P({polyp})}{f_{P}\left( x_{i} \right)}}{{{P({polyp})}{f_{P}\left( x_{i} \right)}} + {{P({normal})}{f_{N}\left( x_{i} \right)}}}},$wherein x_(i) is an N_(c) dimensional vector associated with pixel ithat includes intensity values from different color spaces, P(polyp) andP(normal) denote the prior probability that pixel i is a polyp pixel ora non-polyp pixel respectively, and f_(p) and f_(N) are distributionfunctions with respect to x_(i), given that pixel i is either polyp ornon-polyp, respectively, wherein if g(x_(i),) is less than apredetermined threshold, pixel i is determined to be a non-polyp pixel.3. The method of claim 2, wherein N_(c) =3 is a number of colorchannels, wherein the color channels are (BUH), wherein B is selectedfrom an RGB color space, U is selected from a LUV color space, and H isselected from a HSV color space.
 4. The method of claim 2, whereing(x_(i),) is approximated by$\frac{f_{P}\left( x_{i} \right)}{f_{N}\left( x_{i} \right)}.$
 5. Themethod of claim 1, wherein polyp candidates for an image I_(k) comprisea plurality of ellipses {(E_(k) ^(l),p_(k) ^(l))}_(l=1) ^(N) ^(k) foreach image I_(k), wherein ellipse E_(k) ^(l) is a polypregion-of-interest with a polyp probability P_(k) ^(l) and N_(k) is thenumber of ellipses for image I_(k).
 6. The method of claim 5, whereinfeatures extracted from said polyp candidates include geometric featuresand appearance based features, wherein said geometric features includean area of each ellipse, and a ratio of the major axis length and theminor axis length of each ellipse, and wherein said appearance basedfeatures are extracted from 3 concentric regions-of-interest (ROIs)about the detected ellipse.
 7. The method of claim 6, wherein appearancebased features include a multi-scaled rotational invariant local binarypattern wherein each of a plurality of circular neighbors of a pixel iare labeled as 1 if the intensity of the neighbor is greater than thatof pixel i, and 0 otherwise, and a binary number formed by the values ofthe plurality of neighbors is classified into a bin based on a number ofconsecutive 1′s and consecutive 0′s in each binary number, wherein saidbinary number is classified into a separate bin if there are noconsecutive 1′s and consecutive 0′s in said binary number, wherein eachbin is a feature.
 8. The method of claim 6, wherein appearance basedfeatures include a histogram of oriented gradients (HOG) calculated bycomputing image gradients inside each ROI, and assigning a magnitude ofeach gradient to a direction bin based on an orientation of each polypcandidate ellipse.
 9. The method of claim 8, wherein appearance basedfeatures further include calculating a dissimilarity of HOG featuresbetween two ROIs from d(f,g) =Σ_(i=1) ^(N|f) _(i)−g_(i), wherein f and gare HOG histograms for the two regions, respectively, ∥ is an L₁ norm,and N is a number of direction bins, and calculating a colordistribution dissimilarity between two regions from d(f,g) =Σ_(i=1)^(N)|f_(i)−g_(i)|, wherein f and g are intensity distribution histogramsfor the two regions, respectively, for each color.
 10. The method ofclaim 5, wherein performing a regression on said extracted featurescomprises solving Y≈f (X, β), wherein Y is a target variable, X is avector of the extracted features, and β are predetermined parameterscalculated during a training stage, wherein target value y _(k) ^(l)forthe l-th polyp candidate of image I_(k) is defined as an overlap ratiobetween the ellipse E_(k) ^(l) of the polyp candidate and a ground truthellipse E_(k) ^(g) : $y_{k}^{l} = \left\{ {\begin{matrix}{\min\left( {\frac{{E_{k}^{g}\bigcap E_{k}^{l}}}{E_{k}^{g}},\frac{{E_{k}^{g}\bigcap E_{k}^{l}}}{E_{k}^{l}}} \right)} & {I_{k}\mspace{14mu}{is}\mspace{14mu}{positive}\mspace{14mu}{image}} \\0 & {I_{k}\mspace{14mu}{is}\mspace{14mu}{negative}\mspace{14mu}{image}}\end{matrix},} \right.$ wherein ∥represents an area of the argumentellipse.
 11. The method of claim 10, wherein f (X, β) is a supportvector regressor.
 12. The method of claim 10, further comprisingcalculating an image-wise polyp score S_(k) that represents a likelihoodthat an image I_(k) contains one or more polyps fromS _(k)=Σ_(l=1) ^(Ñ) ^(k) (w·p _(k) ^(l)+(1−w)·y _(k) ^(l)) wherein p_(k)^(l) is the probability of candidate ellipse E_(k) ^(l) being a polypwherein {p_(k) ^(l) }_(l=1) ^(N) ^(k) is sorted in descending order anda top Ñ_(k) detected polyp candidates are selected, y_(k) ^(l) is thecorresponding regression target value, and w is a combining weightdetermined in a training stage to achieve a largest true positive rateat a predetermined false positive rate.
 13. A method for training adetector to detect polyps in endoscopy images obtained from a patient,comprising the steps of: detecting polyp candidates in a plurality oftwo dimensional digitized images received from an endoscopy apparatusbased on results of predefined processing performed on each of saidplurality of two dimensional digitized images, such that polypcandidates for an image I_(k) comprise pixels in a plurality of ellipses{(E_(k) ^(l) ,p_(k) ^(l)) }_(l=1) ^(N) ^(k) for each image I_(k),wherein ellipse E_(k) ^(l) is a polyp region-of-interest with a polypprobability p_(k) ^(l) and N_(k) is the number of ellipses for imageI_(k); extracting features from said polyp candidates based on resultsof predefined processing performed on pixels of said polyp candidates;calculating target values Y for a regression model represented byY≈(X,β), wherein Y is a target variable characteristic of a polypcandidate depicting an actual polyp, as determined by comparing saidextracted features with features for a known actual polyp image, X is avector of features, and β are unknown parameters to be determined;determining the parameters β for the regression model from the targetvalues Y and feature vector X; and using the determined parameters β“totrain the regression model in said detector”.
 14. The method of claim13, wherein target value y_(k) ^(l) for the l-th polyp candidate ofimage I_(k) is defined as an overlap ratio between the ellipse E_(k)^(l) of the polyp candidate and a ground truth ellipse E_(k) ^(g):$y_{k}^{l} = \left\{ {\begin{matrix}{\min\left( {\frac{{E_{k}^{g}\bigcap E_{k}^{l}}}{E_{k}^{g}},\frac{{E_{k}^{g}\bigcap E_{k}^{l}}}{E_{k}^{l}}} \right)} & {I_{k}\mspace{14mu}{is}\mspace{14mu}{positive}\mspace{14mu}{image}} \\0 & {I_{k}\mspace{14mu}{is}\mspace{14mu}{negative}\mspace{14mu}{image}}\end{matrix},} \right.$ wherein ∥represents an area of the argumentellipse.
 15. The method of claim 13, wherein features extracted fromsaid polyp candidates include geometric features and appearance basedfeatures, wherein said geometric features include an area of eachellipse, and a ratio of the major axis length and the minor axis lengthof each ellipse, and wherein said appearance based features areextracted from 3 concentric regions-of-interest (ROIs) about thedetected ellipse.
 16. The method of claim 15, wherein appearance basedfeatures include a multi-scaled rotational invariant local binarypattern wherein each of a plurality of circular neighbors of a pixel iare labeled as 1 if the intensity if the neighbor is greater than thatof pixel i, and 0 otherwise, and a binary number formed by the values ofthe plurality of neighbors is classified into a bin based on a number ofconsecutive 1′s and consecutive 0′s in each binary number, wherein saidbinary number is classified into a separate bin if there are noconsecutive 1′s and consecutive 0′s in said binary number, wherein eachbin is a feature.
 17. The method of claim 15, wherein appearance basedfeatures include a histogram of oriented gradients (HOG) calculated bycomputing image gradients inside each ROI, and assigning a magnitude ofeach gradient to a direction bin based on an orientation of each polypcandidate ellipse.
 18. The method of claim 15, wherein appearance basedfeatures further include calculating a dissimilarity of HOG featuresbetween two ROIs from d(f,g )=Σ_(i=1) ^(N |f) _(i)−g_(i)|wherein f and gare HOG histograms for the two regions, respectively, ∥is an L_(l) norm,and N is a number of direction bins, and calculating a colordistribution dissimilarity between two regions from d(f,g)=Σ_(i=1)^(N |f) _(i)−g_(i)|, wherein f and g are intensity distributionhistograms for the two regions, respectively, for each color.
 19. Anon-transitory program storage device readable by a computer, tangiblyembodying a program of instructions executed by the computer to detectpolyps in endoscopy images obtained from a patient, comprising the stepsof: receiving a plurality of two dimensional digitized images generatedby an endoscopy apparatus; removing from said plurality of twodimensional digitized images images that are unlikely to depict a polyp,based on results of predefined image-wise processing performed on eachof said plurality of two dimensional digitized images, wherein aplurality of candidate images remains that are likely to depict a polyp;from each of said plurality of candidate images, pruning non-polyppixels that are unlikely to be part of a polyp depiction, based onresults of predefined pixel-wise processing performed on each of saidplurality of candidate images; detecting polyp in the pruned candidateimages, based on Results of predefined processing performed on pixels ofsaid pruned candidate images; extracting features from said polypcandidate pixels based on results of predefined predefined processingperformed on said polyp candidate pixels; performing a regression onsaid extracted features to determine whether said polyp candidate pixelsare likely to depict an actual polyp: and identifying candidate imagesas depicting polyps based on results of said regressinn, wherein saididentified candidate imaged are used by a clinician to diagnosis acondition of said patient.
 20. The computer readable program storagedevice of claim 19, wherein pruning non-polyp pixels from the candidateimages comprises calculating a posterior Bayesian probability of a pixeli being a polyp pixel${{{g\left( x_{i} \right)} \equiv {P\left( {x_{i}\mspace{14mu}{is}\mspace{14mu}{polyp}} \right)}} = \frac{{P({polyp})}{f_{P}\left( x_{i} \right)}}{{{P({polyp})}{f_{P}\left( x_{i} \right)}} + {{P({normal})}{f_{N}\left( x_{i} \right)}}}},$wherein x_(i) is an N_(c) dimensional vector associated with pixel ithat includes intensity values from different color spaces, P(polyp) andP(normal) denote the prior probability that pixel i is a polyp pixel ora non-polyp pixel respectively, and f_(p) and f_(N) are distributionfunctions with respect to x_(i), given that pixel i is either polyp ornon-polyp, respectively, wherein if g(x_(i)) is less than apredetermined threshold, pixel i is determined to be a non-polyp pixel.21. The computer readable program storage device of claim 20, whereinN_(c) =3 is a number of color channels, wherein the color channels are(BUH), wherein B is selected from an RGB color space, U is selected froma LUV color space, and H is selected from a HSV color space.
 22. Thecomputer readable program storage device of claim 20, wherein g(x_(i))is approximated by$\frac{f_{P}\left( x_{i} \right)}{f_{N}\left( x_{i} \right)}.$
 23. Thecomputer readable program storage device of claim 19, wherein polypcandidates for an image I_(k) comprise a plurality of ellipses {(E_(k)^(l), p_(k) ^(l))}_(l=1) ^(N) ^(k) for each image I_(k) wherein ellipseE_(k) ^(l) is a polyp region-of-interest with a polyp probability p_(k)^(l) and N_(k) is the number of ellipses for image I_(k).
 24. Thecomputer readable program storage device of claim 23, wherein featuresextracted from said polyp candidates include geometric features andappearance based features, wherein said geometric features include anarea of each ellipse, and a ratio of the major axis length and the minoraxis length of each ellipse, and wherein said appearance based featuresare extracted from 3 concentric regions-of-interest (ROIs) about thedetected ellipse.
 25. The computer readable program storage device ofclaim 24, wherein appearance based features include a multi-scaledrotational invariant local binary pattern wherein each of a plurality ofcircular neighbors of a pixel i are labeled as 1 if the intensity of theneighbor is greater than that of pixel i, and 0 otherwise, and a binarynumber formed by the values of the plurality of neighbors is classifiedinto a bin based on a number of consecutive l′s and consecutive 0′s ineach binary number, wherein said binary number is classified into aseparate bin if there are no consecutive 1′s and consecutive 0′s in saidbinary number, wherein each bin is a feature.
 26. The computer readableprogram storage device of claim 24, wherein appearance based featuresinclude a histogram of oriented gradients (HOG) calculated by computingimage gradients inside each ROI, and assigning a magnitude of eachgradient to a direction bin based on an orientation of each polypcandidate ellipse.
 27. The computer readable program storage device ofclaim 26, wherein appearance based features further include calculatinga dissimilarity of HOG features between two ROIs from d(f,g)=Σ_(i=1)^(N)|g_(i)−g_(i)|, wherein f and g are HOG histograms for the tworegions, respectively, ∥is an L_(l) norm, and N is a number of directionbins, and calculating a color distribution dissimilarity between tworegions from d(f,g)=Σ_(i=1) ^(N)|f_(i)−g_(i)|wherein f and g areintensity distribution histograms for the two regions, respectively, foreach color.
 28. The computer readable program storage device of claim23, wherein performing a regression on said extracted features comprisessolving Y≈(X,β), wherein Y is a target variable, X is a vector of theextracted features, and β are predetermined parameters calculated duringa training stage, wherein target value y_(k) ^(l) for the l-th polypcandidate of image I_(k) is defined as an overlap ratio between theellipse E_(k) ^(l) of the polyp candidate and a ground truth ellipseE_(k) ^(g): $y_{k}^{l} = \left\{ {\begin{matrix}{\min\left( {\frac{{E_{k}^{g}\bigcap E_{k}^{l}}}{E_{k}^{g}},\frac{{E_{k}^{g}\bigcap E_{k}^{l}}}{E_{k}^{l}}} \right)} & {I_{k}\mspace{14mu}{is}\mspace{14mu}{positive}\mspace{14mu}{image}} \\0 & {I_{k}\mspace{14mu}{is}\mspace{14mu}{negative}\mspace{14mu}{image}}\end{matrix},} \right.$ wherein ∥represents an area of the argumentellipse.
 29. The computer readable program storage device of claim 28,wherein f (X, β) is a support vector regressor.
 30. The computerreadable program storage device of claim 28, the method furthercomprising calculating an image-wise polyp score S_(k) that represents alikelihood that an image I_(k) contains one or more polyps fromS _(k)=Σ_(l=1) ^(Ñ) ^(k) (w·p _(k) ^(l)+(1−W)·y _(k) ^(l)), whereinp_(k) ^(l) is the probability of candidate ellipse E_(k) ^(l) being apolyp wherein {p_(k) ^(l)}_(l=1) ^(N) ^(k) is sorted in descending orderand a top Ñ_(k) detected polyp candidates are selected, y_(k) ^(l) isthe corresponding regression target value, and w is a combining weightdetermined in a training stage to achieve a largest true positive rateat a predetermined false positive rate.